Taylor Luttrell-Williams writes that LLM coding agents create a new secrets-leakage pathway: they read local files (env files, credential profiles, SSH configs) as context and forward that content to external model providers before any traditional security gate—commit, pull request, or CI—can catch it. The article argues organizations should treat agent context as an egress surface and apply deterministic, model-independent secrets detection at the moment an agent decides what to read and transmit.
- The 2025 Verizon DBIR reports a 94-day median to remediate leaked secrets found in GitHub repos, but agent workflows make that window moot because the secret has already left the local environment.
- Supply-chain campaigns like Mini Shai-Hulud specifically target coding-tool configuration files, making agent context an active attack surface.
- Sonar's agent plugins for Claude Code, Copilot, Codex, and Cursor block credential-shaped values in prompts and file reads before they reach a model provider.
- The 2025 Verizon DBIR reports a 94-day median to remediate leaked secrets found in GitHub repos, but agent workflows make that window moot because the secret has already left the local environment.
- Supply-chain campaigns like Mini Shai-Hulud specifically target coding-tool configuration files, making agent context an active attack surface.
- Sonar's agent plugins for Claude Code, Copilot, Codex, and Cursor block credential-shaped values in prompts and file reads before they reach a model provider.
Ashley writes about Needle 2, a 14MB function-calling LLM from Cactus Compute that converts plain English prompts into local actions on a Raspberry Pi 5 using CPU alone. Rather than acting as a general chatbot, the model is purpose-built to select from declared Python functions and fill in their arguments, running entirely offline after a one-time download. In benchmark runs, inference latency ranges from 76 to 149 milliseconds, and the model correctly refuses questions outside its declared tool set.
- Native session is ~28MB; the full Python process peaks at 43–46.4MB
- Weights and code released under Apache 2.0 on Hugging Face and GitHub
- Can be fine-tuned locally on a laptop for a specific set of tools
- Eben Upton's endorsement: "Needle 2 is rather excellent"
- Native session is ~28MB; the full Python process peaks at 43–46.4MB
- Weights and code released under Apache 2.0 on Hugging Face and GitHub
- Can be fine-tuned locally on a laptop for a specific set of tools
- Eben Upton's endorsement: "Needle 2 is rather excellent"
Abid Ali Awan writes a tutorial showing how to wrap existing Python functions as tools for an LLM agent using the OpenAI Agents SDK. The process involves decorating a function with `@function_tool`, defining an `Agent` with instructions and a tools list, and letting the `Runner` manage the loop where the model decides which tools to call, what arguments to pass, and when to stop. The example uses a simple website-latency checker that becomes an agent capable of comparing response times across multiple URLs and explaining results in natural language.
- The SDK auto-generates the JSON tool schema from the function signature and docstring; no manual schema is needed.
- The same pattern applies to CSV analysis, server monitoring, log analysis, and API automation.
- Cheaper models such as GPT-5.6 Luna make multi-agent tool-calling systems more affordable at scale.
- The SDK auto-generates the JSON tool schema from the function signature and docstring; no manual schema is needed.
- The same pattern applies to CSV analysis, server monitoring, log analysis, and API automation.
- Cheaper models such as GPT-5.6 Luna make multi-agent tool-calling systems more affordable at scale.
yibie maintains a curated awesome list of over 300 public projects built on Jev, TypeSafe AI's System One model that takes unstructured state plus a typed question and returns a typed decision (Choice, Score, or Noul) with a confidence value in a single forward pass. The list spans 14 categories from classification and routing to agent safety guardrails, database extensions, and open-source replicas, enforcing inclusion rules that require public, citable sources demonstrating a genuine typed-decision use of Jev.
- The repo explicitly warns that same-day bulk submissions from one author sharing a scaffold can satisfy every inclusion rule while remaining unproven, and treats volume as not evidence of quality
- Open replicas range from 547k parameters (minojev) to 395M (von); one 706K-parameter model beats Jev at form-filling (99.7% vs 83.6%)
- Jev performs no token generation; its speed comes from parallel constrained decoding, so an inference engine can expose a Jev-like API over any open-weight model
- A CAPTCHA arbitrage example illustrates per-decision pricing: solving at $0.0068 per hundred against a marketplace paying a cent each
- LangChain published both a Jev harness wiring guide and an independent evaluation concluding Jev is the cheaper and more consistent judge for online evals versus LLM judges
- The repo explicitly warns that same-day bulk submissions from one author sharing a scaffold can satisfy every inclusion rule while remaining unproven, and treats volume as not evidence of quality
- Open replicas range from 547k parameters (minojev) to 395M (von); one 706K-parameter model beats Jev at form-filling (99.7% vs 83.6%)
- Jev performs no token generation; its speed comes from parallel constrained decoding, so an inference engine can expose a Jev-like API over any open-weight model
- A CAPTCHA arbitrage example illustrates per-decision pricing: solving at $0.0068 per hundred against a marketplace paying a cent each
- LangChain published both a Jev harness wiring guide and an independent evaluation concluding Jev is the cheaper and more consistent judge for online evals versus LLM judges
Rodney Brooks writes about the seven cognitive errors that lead to wildly mistaken predictions about the future of robotics and artificial intelligence. He frames four categories of predictions—Artificial General Intelligence, the Singularity, misaligned values, and evil AI entities—then dissects the reasoning failures behind them: overestimating short-term impact while underestimating long-term effects, treating far-future technology as unfalsifiable magic, confusing narrow task performance with broad competence, relying on ambiguous "suitcase words" like "learn" or "understand," extrapolating exponentials that will inevitably flatten, imagining Hollywood-style single-disruption scenarios, and ignoring the glacial pace of hardware deployment in the physical world.
- The OpenWorm project spent thirty years attempting to simulate C. elegans (302 neurons, 7,000 connections) bottom-up and was not yet halfway done as of 2017.
- iPod storage followed a perfect five-year exponential (10→160 GB) then collapsed abruptly once a single device could hold a complete music library.
- Modern factory automation still relies on PLCs introduced in 1968 that emulate electromechanical relays; Tesla was actively hiring PLC technicians at its Fremont factory.
- Autonomous vehicles drove on public roads in 1987 and coast-to-coast across the US in 1995, yet no large-scale deployment path had been identified by 2017.
- Brooks observes that humans coexisted with horses—autonomous agents with ongoing existences and super-human strength—for millennia without a single formal theorem about them.
- The OpenWorm project spent thirty years attempting to simulate C. elegans (302 neurons, 7,000 connections) bottom-up and was not yet halfway done as of 2017.
- iPod storage followed a perfect five-year exponential (10→160 GB) then collapsed abruptly once a single device could hold a complete music library.
- Modern factory automation still relies on PLCs introduced in 1968 that emulate electromechanical relays; Tesla was actively hiring PLC technicians at its Fremont factory.
- Autonomous vehicles drove on public roads in 1987 and coast-to-coast across the US in 1995, yet no large-scale deployment path had been identified by 2017.
- Brooks observes that humans coexisted with horses—autonomous agents with ongoing existences and super-human strength—for millennia without a single formal theorem about them.
TianyuCodings writes about JevHarness, a system where an LLM authors a task-specific harness for Jev (a lightweight judgment model from TypeSafe). The harness converts task observations into features, constructs Jev questions and criteria, and combines structured answers into actions. Once the harness is frozen, execution runs only the harness code and Jev calls without the authoring LLM on every decision. An optional GEPA evolution loop refines the harness using rewards and complete execution traces.
- Pokemon eval: 5 reflection rounds improved win rate from 25% (3/12) to 75% (9/12); the round-3 candidate was selected as best
- Selected harness median full decision: 568 ms (P95 657 ms); individual Jev request median: 269 ms (P95 348 ms)
- Installable as a Claude Code plugin or Codex skill; the skill guides the agent to clarify task inputs, legal actions, and success criteria before building
- Requires Python 3.11+; functional Python nodes need a macOS native sandbox and fail closed if unavailable
- The archived website and recorded-call inspection require no model credentials
- Pokemon eval: 5 reflection rounds improved win rate from 25% (3/12) to 75% (9/12); the round-3 candidate was selected as best
- Selected harness median full decision: 568 ms (P95 657 ms); individual Jev request median: 269 ms (P95 348 ms)
- Installable as a Claude Code plugin or Codex skill; the skill guides the agent to clarify task inputs, legal actions, and success criteria before building
- Requires Python 3.11+; functional Python nodes need a macOS native sandbox and fail closed if unavailable
- The archived website and recorded-call inspection require no model credentials
jev-social is an open-source tool that pairs the Jev model (from Typesafe AI) with the socai browser CLI to perform grounded social media research on Instagram, TikTok, and LinkedIn. Jev makes sequential decisions about which operation to perform next (search, open a profile, read comments, download media), while socai executes each chosen command in a real Chrome instance and returns the result before Jev makes the next decision.
- Packaged as a Codex plugin and installable via `npx` or the cross-agent Skills CLI
- Each step's choice, confidence, command, and elapsed time are stored for auditability
- Enforces read-only and login-gate boundaries; unsupported or low-confidence decisions are rejected rather than executed
- A recorded Instagram run produced four source-linked records in ~64 seconds
- Runs loopback-only on localhost:8766 and requires an OpenRouter API key with Jev access
- Packaged as a Codex plugin and installable via `npx` or the cross-agent Skills CLI
- Each step's choice, confidence, command, and elapsed time are stored for auditability
- Enforces read-only and login-gate boundaries; unsupported or low-confidence decisions are rejected rather than executed
- A recorded Instagram run produced four source-linked records in ~64 seconds
- Runs loopback-only on localhost:8766 and requires an OpenRouter API key with Jev access
Dev Agrawal writes about Stanley, a command-line tool that routes natural-language requests about code changes to deterministic workflows. Each workflow gathers bounded evidence from a Git diff or log and asks the TypeSafe Jev model fixed-choice questions about it; the tool's own code applies thresholds to make decisions, never delegating that step to the model. When no installed workflow covers a request, Stanley falls back to the Pi coding agent and then queues a background job to draft a new workflow for that kind of request, which the user can promote into a permanent, agent-free path.
- Ten built-in workflows: check, review, test gaps, security, performance, compatibility, summarization, code search, failure triage, and comment triage
- An empty findings list is explicitly not an approval; every report includes a `notChecked` field
- Repository workflows under `.stanley/workflows/` run in-process with full user privileges and cannot declare CLI flags
- Agent-written workflows are quarantined until explicitly promoted via `--promote-candidate`; the agent cannot self-activate
- 0.1.0 is not yet on npm; the old `jev-code` placeholder package (0.0.1) does nothing
- 110 stars, 6 forks, single contributor, MIT licensed, 100% TypeScript, no releases published
- Ten built-in workflows: check, review, test gaps, security, performance, compatibility, summarization, code search, failure triage, and comment triage
- An empty findings list is explicitly not an approval; every report includes a `notChecked` field
- Repository workflows under `.stanley/workflows/` run in-process with full user privileges and cannot declare CLI flags
- Agent-written workflows are quarantined until explicitly promoted via `--promote-candidate`; the agent cannot self-activate
- 0.1.0 is not yet on npm; the old `jev-code` placeholder package (0.0.1) does nothing
- 110 stars, 6 forks, single contributor, MIT licensed, 100% TypeScript, no releases published
Mark Marosi writes about decider, a family of models fine-tuned from Qwen3.5 that produce typed decisions (choice, score, boolean) in a single forward pass without text generation, returning calibrated probability distributions over user-defined options. The project is an open reproduction of TypeSafe AI's "System One" model class (Jev), released in sizes from 0.8B to 35B mixture-of-experts with 3B active parameters.
- The schema cache stores K/V states for repeated question prefixes, achieving up to 19x speedup on large option sets by running only the state per request
- v10 adds calibration-aware RL on live MiniWoB++ browser tasks and exact games, lifting browser accuracy from 83% to 93% and halving the belief gap
- TypeSafe's SDKs work unchanged by pointing TYPESAFE_BASE_URL at the decider server
- The 35B model outperforms the 2B on 93 of 95 regression tasks but costs 3-4x per decision and lacks the RL stage
- Co-developed with Claude (Anthropic) as a listed co-author on commits
- The schema cache stores K/V states for repeated question prefixes, achieving up to 19x speedup on large option sets by running only the state per request
- v10 adds calibration-aware RL on live MiniWoB++ browser tasks and exact games, lifting browser accuracy from 83% to 93% and halving the belief gap
- TypeSafe's SDKs work unchanged by pointing TYPESAFE_BASE_URL at the decider server
- The 35B model outperforms the 2B on 93 of 95 regression tasks but costs 3-4x per decision and lacks the RL stage
- Co-developed with Claude (Anthropic) as a listed co-author on commits
Usenet-Rewind is a search platform that indexes Usenet newsgroup archives dating from 1981 to the present, covering early tech support discussions, scientific and academic exchanges, hobbyist communities, and firsthand accounts of internet history. Operated by Erie Data Systems, LLC, the service currently holds approximately 1.09 billion messages with a retention period exceeding 45 years.
- 12.37 million new messages added recently
- Offers API access for programmatic search
- Supports advanced filtering by title, body, author, message ID, newsgroup, and date range
- Provides a browsable timeline of Usenet topics, people, and events by decade
- 12.37 million new messages added recently
- Offers API access for programmatic search
- Supports advanced filtering by title, body, author, message ID, newsgroup, and date range
- Provides a browsable timeline of Usenet topics, people, and events by decade
